There is no shortage of discussion these days about Manufacturing 4.0, IoT, the Digital Transformation journey, and other forward-looking concepts. It’s all very heady and exciting — way up in the clouds, both metaphorically and quite literally in terms of IT infrastructure.

With so many futuristic ideas and buzzwords swirling around, it’s helpful to bring the discussion back down to earth. Let’s drill a little deeper and explore where things actually stand, what the challenges are, and where solutions can be found.

What is the state of manufacturing data today? Let’s start with some good news:

Congratulations! You’re Already Digitized

The goal of gathering shop floor data from processes, machines, and robots has largely been achieved over the past few years. With an abundance of real-time production data streaming in daily, manufacturers would be well-advised to tackle the next priority: understanding their existing data landscape and learning how to make it useful. Because unfortunately, manufacturing data is not yet driving significant business impact. Why is this so?

Enormous Volumes of Data… But Very Little to Show for It

The real challenge is that manufacturing data and the technology surrounding it are fragmented and siloed. Each variety of data comes in its own format and structure, and resides in a specialized system that doesn’t talk to its many counterparts. As a result, most inputs can’t be retrieved in real time. Only a tiny fraction of data is readily accessible, and it takes hours or days. Some manufacturers have aggregated various inputs into a cloud-based data lake. But with other data still siloed on the shop floor, the end result is more akin to a data swamp.

Create a Unified Data Model

To be rendered useful, your diverse data sources — IoT sensors delivering real-time streams, raw materials data, quality input, and energy consumption data — must be stitched together. Barriers need to be broken down so information can be blended, unified, and correlated. This is easier said than done, with volumes in terabytes and variables in the hundreds to tens of thousands. Even if you could hire an army of data scientists, they couldn’t possibly meld everything together in anything close to real-time. After all, data scientists are only human. But AI isn’t.

The Importance of Leveraging AI

Mammoth data projects — involving oceans of input and thousands of variables — are precisely what AI and machine learning were invented for. Leverage these technologies to clean, blend, and interrelate your diverse data sources, and model them into a digital replica of your entire manufacturing process: a “digital twin.” With your data universe unified and modeled in this way, you can then turn advanced algorithms loose. Drilling down to specific machines, processes, and parts, sophisticated analysis techniques will reveal solutions to critical questions that had previously remained unanswered.

Of course, you’ll need the help of an analytics or IoT platform that can do all this, such as Sight Machine, an AI-driven application that is already achieving key results for manufacturers across industries.

To recap: Unify your data, model all of production, and analyze away. That’s the prescription. Now, how do you go about applying it? Where do you even begin?

Start small. Begin with one production line or plant, and determine the goals you’d like to achieve with data. Reducing your scrap rate could be one. Optimizing preventive maintenance might be another. You may want to finally pinpoint the root causes of troublesome recurring outages. Or determine optimum machine settings to generate uniform product quality. These are just examples; choose your own objectives based on your most pressing issues and needs.

Once you have a plant and a plan, deployment won’t take an army of consultants. Neither will it take years to realize value. A small team working smartly and steadily can achieve demonstrable and dramatic outcomes in a few weeks or a few months at most.

Learn fast. With evidence-based results arriving so quickly, implementation can be just as rapid. Working from a single source of truth — a comprehensive model encompassing all operational inputs — everyone from plant directors, line supervisors, process engineers, and operators can immediately start learning, adjusting, and iteratively fine-tuning their processes and procedures based on what the data has revealed.

Scale up quickly. Once you’re past the single-plant POC stage, it’s important to quickly replicate implementation across facilities. You want to get all of them on the same technology, and the same version. Here too, the Sight Machine platform is an excellent choice, as it’s engineered for rapid multi-factory deployment.

Now Is the Time

The great majority of manufacturers have yet to put production data to work to drive tangible impact. To hark back to our examples, too many outages still occur, scrap rates could be lower, and numerous pesky operational problems remain unsolved. Of course, even with all this, your company may still be doing quite well financially. To which I’d observe that now — when profits and performance are strong — is the best time to embrace Manufacturing 4.0 and starting turning your data into business value. In this era of economic uncertainty, no one knows for sure what the future may bring. But odds are excellent that the new business value you build today will be the engine that powers your prosperity and growth tomorrow.

Carlo Adalberto Moretti is a Microsoft Industry Executive for Manufacturing and Resources with Microsoft's Western European Enterprise Commercial organization. In this role, he works with CXOs to accelerate their organization's digital transformation journey.

Daya Vivek is Director, Platform Engineering at Sight Machine. She has a a 20 year track record in delivering enterprise software products and engaging customers to meet diverse market and user needs. Previously, Daya had 17 year career at IBM spanning multiple roles: an Engineering Manager for the Watson Discovery Service hosted on the IBM cloud, a Solution Architect for the Customer Enablement team for IBM’s Big Data Solution (Infosphere Biginsights), and a software developer in pureQuery (a data access platform for database clients) and IBM’s database engine DB2. Daya has a Master’s in Computer Science from Arizona State University and an MBA from Santa Clara University.

Josh Brown

DevOps Engineering Manager

Josh Brown is an Engineering Manager focusing on infrastructure and tooling for Sight Machine. For the last 15 years, Josh has worked to help scale multiple startups across many different industries, from fashion to mobile messaging. Josh uses his experience from previous startups to solve nuanced problems that span well beyond the implementation of technology.

In his personal life, Josh has also been involved in multiple humanitarian efforts, in Mexico and Haiti, and enjoys exploring the world with his family.

Ed Jimenez

VP of Marketing

Ed Jimenez is VP of Marketing for Sight Machine. Previously, Mr. Jimenez led Cisco’s Enterprise and Industry Marketing teams. He also worked as a senior consultant helping Cisco’s largest customers understand how disruptive technologies affect the customer experience. Prior to joining Cisco, Mr. Jimenez led Gartner’s Retail & Consumer Products Practice. He also spent a number of years in the retail and manufacturing industries with positions in technology transformation and operations.

Mr. Jimenez has published a number of papers on retail & manufacturing technology trends and was a regular host for the NBC Morning News Technology Report.

Mr. Jimenez earned his M.B.A. from the University of Illinois.

Harry Wornick

Director of Product

Harry Wornick is the Director of Product for Sight Machine. For the past several years, Mr. Wornick has led Sight Machine’s product efforts, from infrastructure and data pipeline, to visualization and analytics. Previously, Mr. Wornick was the Senior Product Manager at Support.com, leading the development of cloud-based customer support software.

Mr. Wornick earned his B.S. in Engineering from Harvey Mudd College, where he spent several years working with national laboratories on renewable energy research.

Ajay Nayak

Product Engineering Manager

Ajay Nayak is the Product Engineering Manager for Sight Machine. Previously, he was VP of Engineering for Bakround, a startup focused on improving the recruiting process for hiring managers and candidates using machine learning. Prior to that, he led an engineering team for Insightly, an SMB-focused CRM. He also has consulting experience at Booz Allen Hamilton and Slalom, which has enabled him to gain expertise in process improvement for a variety of industries.

Ajay has a BS in Electrical & Computer Engineering from Rutgers University, and an MEng in Systems Engineering from Stevens. He’s passionate about using technology to measurably improve societal outcomes and is actively involved in youth-oriented volunteering for his local community.

Kurt DeMaagd, PhD

Chief AI Officer & Co-Founder

Kurt co-founded Slashdot.org and has served as a professor at Michigan State University in information management, economics, and policy. Kurt is an accomplished analytics programmer.

Chris Dobbrow

SVP, Sales

Chris has over 25 years of strategic enterprise sales experience creating & executing successful go to market strategies focused on enterprise customers. He has served in senior management roles at Perforce Software, SourceForge and Ziff-Davis.

Jon Sobel

CEO & Co-Founder

Jon has served on the management teams of several companies in pioneering industries, including Tesla Motors, SourceForge, and in its early years, Yahoo! Jon holds an BA from Princeton, a JD from the University of Michigan, and an MBA from Wharton.

Adam Taisch

VP of Global Sales & Co-Founder

Adam has led business development, product development and sales in innovative industries for 15 years. An early employee at Yahoo!, and a proud son of the Midwest, Adam excels at ensuring the new technologies serve client needs.

Anthony Oliver

Lead Applications Engineer & Co-Founder

Anthony has over 12 years of experience developing and deploying robotics, computer vision, and data analysis tools in the manufacturing sector. He is a multidisciplinary software engineer who is equally at home in application engineering, dev-ops, front and back end development, and vision programming.

Jerry Wu

CFO

Jerry has over 20 years of experience in technology corporate finance and public/private equity investments. Prior to his career in finance he was a manufacturing engineer with Silicon Graphics. Jerry holds BS and MS degrees in electrical engineering from Stanford and an MBA from Wharton. He is a CFA charterholder.

John Stone

VP of Business Development & Partnerships

An experienced business development executive who has worked with global brands to drive engagement, collaboration, and results across organizations at all levels.

Beth Crane

VP of Data

Beth Crane, PhD is Vice President of Data for Sight Machine, the category leader in manufacturing analytics. In this role she focuses on helping manufacturers understand how advanced analytical techniques can solve complex problems in production and operations.

Prior to Sight Machine, Beth has worked in both academia and industry and has led the development of analytical and reporting tools used for continuous process improvement.

She received her PhD and Masters of Science degrees from the University of Michigan and was awarded a National Science Foundation postdoctoral fellowship to explore the development of statistical methods for predicting dysfunction in multi-dimensional time series data.

Sudhir Arni

VP of Implementation

Sudhir Arni​ is Sight Machine’s VP of Manufacturing Transformation. Prior to joining Sight Machine, Sudhir was an engagement manager at McKinsey & Co., where he designed and led manufacturing transformation programs for pharmaceutical and chemical manufacturers. He received joint MBA and Master of Science degrees from the Kellogg School of Management and McCormick School of Engineering at Northwestern University.

Nathan Oostendorp

CTO & Co-Founder

Nathan Oostendorp is the CTO of Sight Machine, he co-founder the company in 2011. Nathan started his career as a controls engineer at Donnelly Corporation (now Magna Mirror) where he worked on PLC programming, computer vision, data acquisition, and robotics for a major automotive supplier.

In 1996 he co-founded Slashdot.org, a major tech news blog which was the center of the Linux and Open Source Software movement. During this period he spun off several other successful open online communities including Everything2.com (an early precursor to Wikipedia) and PerlMonks.org, the central hub for the Perl programming Language. He also created the first Open Source advertising and analytics platform. He then joined SourceForge.net as the site architect and ushered it through a period of growth where it became a top 100 website globally, and hosted several hundred thousand software projects.

He holds a BS in Computer Science from Hope College in Holland Michigan, and an MSI in Information Science from the University of Michigan.

10 Hot AI-powered IoT startups

The Internet of Things generates a lot of data that needs to be processed, and innovative startups recognize that artificial intelligence can lighten the load. Jeff Vance of Network World selected Sight Machine as a 10 hot AI-powered IoT startup. Read on to learn more about what Sight Machine does to address this.

Problem Sight Machine solves: Manufacturers struggle to make optimum decisions quickly. When dealing with problems that emerge on the plant floor, any indecision or delay in decision making could be costly.

In manufacturing, data variety (due to thousands of sources) is far greater than in other IoT use cases, and according to research from Morgan Stanley, the sheer quantity of data is also larger than anywhere else. Traditional analytics tools can’t cope with either the variety or volume.

How they solve it: Sight Machine software uses canonical data models and AI to ingest, integrate, and map massive amounts of heterogeneous data into operational models. The canonical data models represent any machine, line, facility, supplier, part or batch that the manufacturer specifies. Once modeled, data is then systematically and continuously analyzed.

By standardizing the manufacturing models and following a data-first approach to decision making, Sight Machine enables manufacturers to automate data ingestion in a rapid, highly repeatable manner. The standardized model allows manufacturers to create downstream applications that immediately leverage the modeled data.

Analytical techniques include advanced inferential statistics, machine learning and AI, all of which are applied to generate manufacturing-specific insights. Within its platform, Sight Machine analyzes and visualizes data, so results can be viewed via a browser on any connected device.

Why they’re a hot startup to watch: Sight Machine has the deepest pockets in this roundup, backed by $50 million in VC funding. CEO and co-founder Jon Sobel was previously with Tesla and Yahoo, while co-founder and CTO Nathan Oostendorp and co-founder and Chief Data Scientist Kurt DeMaagd previously co-founded Slashdot.org. Finally, the customers Sight Machine has accumulated are impressive, including GE, Fiat Chrysler, and Fujitsu.

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